Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation
Abstract In vitro fertilisation (IVF) comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient’s uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-01-01
|
| Series: | Diagnostic and Prognostic Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41512-020-00091-2 |
| _version_ | 1818598638195048448 |
|---|---|
| author | Jack Wilkinson Andy Vail Stephen A. Roberts |
| author_facet | Jack Wilkinson Andy Vail Stephen A. Roberts |
| author_sort | Jack Wilkinson |
| collection | DOAJ |
| description | Abstract In vitro fertilisation (IVF) comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient’s uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. As such, the ability to predict not only the overall outcome of the cycle, but also the stage-specific responses, can be useful. This could be done by developing separate models for each response variable, but recent work has suggested that it may be advantageous to use a multivariate approach to model all outcomes simultaneously. Here, joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). A further consideration is whether and how to incorporate information about the response at each stage in models for subsequent stages. We develop a case study using routinely collected data from a large reproductive medicine unit in order to investigate the feasibility and potential utility of multivariate prediction in IVF. We consider two possible scenarios. In the first, stage-specific responses are to be predicted prior to treatment commencement. In the second, responses are predicted dynamically, using the outcomes of previous stages as predictors. In both scenarios, we fail to observe benefits of joint modelling approaches compared to fitting separate regression models for each response variable. |
| first_indexed | 2024-12-16T12:06:53Z |
| format | Article |
| id | doaj.art-c2095b1b66824c0eb495974a5d2c980b |
| institution | Directory Open Access Journal |
| issn | 2397-7523 |
| language | English |
| last_indexed | 2024-12-16T12:06:53Z |
| publishDate | 2021-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic and Prognostic Research |
| spelling | doaj.art-c2095b1b66824c0eb495974a5d2c980b2022-12-21T22:32:17ZengBMCDiagnostic and Prognostic Research2397-75232021-01-015111310.1186/s41512-020-00091-2Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisationJack Wilkinson0Andy Vail1Stephen A. Roberts2Centre for Biostatistics, Division of Population Health, Health Services Research, and Primary Care, Manchester Academic Health Science Centre, University of ManchesterCentre for Biostatistics, Division of Population Health, Health Services Research, and Primary Care, Manchester Academic Health Science Centre, University of ManchesterCentre for Biostatistics, Division of Population Health, Health Services Research, and Primary Care, Manchester Academic Health Science Centre, University of ManchesterAbstract In vitro fertilisation (IVF) comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patient’s uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. As such, the ability to predict not only the overall outcome of the cycle, but also the stage-specific responses, can be useful. This could be done by developing separate models for each response variable, but recent work has suggested that it may be advantageous to use a multivariate approach to model all outcomes simultaneously. Here, joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). A further consideration is whether and how to incorporate information about the response at each stage in models for subsequent stages. We develop a case study using routinely collected data from a large reproductive medicine unit in order to investigate the feasibility and potential utility of multivariate prediction in IVF. We consider two possible scenarios. In the first, stage-specific responses are to be predicted prior to treatment commencement. In the second, responses are predicted dynamically, using the outcomes of previous stages as predictors. In both scenarios, we fail to observe benefits of joint modelling approaches compared to fitting separate regression models for each response variable.https://doi.org/10.1186/s41512-020-00091-2In vitro fertilisationJoint modellingmixed dataSequential predictionMultistage treatment dataMultivariate responses |
| spellingShingle | Jack Wilkinson Andy Vail Stephen A. Roberts Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation Diagnostic and Prognostic Research In vitro fertilisation Joint modelling mixed data Sequential prediction Multistage treatment data Multivariate responses |
| title | Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation |
| title_full | Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation |
| title_fullStr | Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation |
| title_full_unstemmed | Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation |
| title_short | Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation |
| title_sort | multivariate prediction of mixed multilevel sequential outcomes arising from in vitro fertilisation |
| topic | In vitro fertilisation Joint modelling mixed data Sequential prediction Multistage treatment data Multivariate responses |
| url | https://doi.org/10.1186/s41512-020-00091-2 |
| work_keys_str_mv | AT jackwilkinson multivariatepredictionofmixedmultilevelsequentialoutcomesarisingfrominvitrofertilisation AT andyvail multivariatepredictionofmixedmultilevelsequentialoutcomesarisingfrominvitrofertilisation AT stephenaroberts multivariatepredictionofmixedmultilevelsequentialoutcomesarisingfrominvitrofertilisation |